In microarray experiments, one way to increase the detection of rarer RNAs in a population is to use methods that label small portions of only some of the RNAs. These "low complexity representations" (LCRs) increase the sensitivity of detection of the RNAs selected and may also reduce any part of the background contributed by target complexity. We have used PCR of RNA with arbitrary primers (RAP) to enrich for subsets of RNA populations. The method selects internal amplicons in RNAs based on chance matches with the primers, and is simple and robust. We have demonstrated that LCRs can improve mRNA detection limits by an order of magnitude compared to total RNA on conventional glass slide arrays or Affymetrix arrays, while preserving the ratio of expression differences between two samples. We propose to expand the utility of LCR methods in four aims. (1) Build cheap custom arrays that are designed to maximize coverage by RAP LCRs. This will allow rare mRNAs to be detected. (2) Construct "intron" arrays for LCRs that, due to their increased sensitivity, will allow measurement of the level of nascent transcripts (hnRNAs). When hybridized to an appropriate array, each LCR may be able to detect 30% or more of the nascent transcripts in the cell, including some of the rarest. This will improve upon current methods for monitoring RNAs that, until now, have primarily focused on the abundance of the mature mRNA in the cell. (3) Generate LCRs by PCR of restriction fragment subsets of the RNA population. (4) A linear amplification strategy will be applied to make an LCR from sequences adjacent to dispersed repeats in hnRNA. This LCR will be hybridized to the appropriate custom array. The results of the four aims will be compared. Then, in Aim (5) the best LCR strategy, and the corresponding optimized array, will be applied to study nascent transcription and rare mRNAs in the cell cycle in human cell line models. Discoveries of new regulation can be integrated into this well characterized system, and previously known regulated genes can be parsed according to transcriptional vs. post-transcriptional components of their regulation by comparing nascent transcription to steady state RNA levels.